Rufous-and-white wrens Thryophilus rufalbus do not exhibit a dear enemy effects towards conspecific or heterospecific competitors
Bibliographic record
Abstract
Abstract Many territorial animals exhibit reduced aggression towards neighbours. Known as “the dear enemy effect”, this phenomenon has been documented among conspecific animals across a wide range of animal taxa. In theory, the dear enemy effect can also exist between individuals of different species, particularly when those species compete for shared resources. To date, a heterospecific dear enemy effects has only been documented in ants. In this study, we test for both a conspecific and heterospe-cific dear enemy effect in neotropical rufous-and-white wrens Thryophilus rufalbus. This species competes for resources with banded wrens Thryophilus pleurostictus, a closely related sympatric congener. We used acoustic playback to simulate ruf-ous-and-white wren and banded wren neighbours and non-neighbours at the edges of rufous-and-white wren territories. Ruf-ous-and-white wrens responded more strongly to signals from their own species, demonstrating that resident males discriminate between conspecific and heterospecific rivals. They did not, however, exhibit a conspecific dear enemy effect. Further, they did not exhibit a heterospecific dear enemy effect. This could be due to neighbours and non-neighbours posing similar levels of threat in this system, to the possibility that playback from the edges of the subjects’ large territories did not simulate a threatening signal, or to other factors. Our study provides the first test of a heterospecific dear enemy effect in vertebrates, and presents a valuable experimental approach for testing for a heterospecific dear enemy effect in other animals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".